Open AccessDeep Learning for HRRP-based Satellite Recognition
Author(s) -
Lu Wang,
Yuru Zhang,
Yi Huo,
Cunbao Lin
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012002
Subject(s) - artificial intelligence , computer science , deep learning , pattern recognition (psychology) , robustness (evolution) , artificial neural network , support vector machine , data pre processing , preprocessor , feature extraction , radar , dropout (neural networks) , data set , machine learning , telecommunications , biochemistry , chemistry , gene
An approach that employs deep learning technology is presented to recognize satellites based on radar high-resolution range profile (HRRP) data. We focus on extracting effective satellite recognition features in this paper. Thus, a deep learning model is constructed by gated recurrent unit (GRU) neural network and support vector machine (SVM) to extract more abstract and accurate features. Firstly, the radar HRRP data of four satellites is obtained by simulation. And data preprocessing has been done according to HRRP characteristic. Next, a GRU-SVM model is set up and some deep learning skills, such as dropout and cross validation, have been applied to improve recognition accuracy. The training results of GRU neural network show their effectiveness. In order to demonstrate the superiority of this approach, five other feature extraction methods have been used as a comparison based on clean satellite HRRP data and noisy data. The experiment results show that the presented GRU-SVM model could recognize satellites effectively and accurately, and has better recognition performance and noise robustness compared with five other methods.